Before the actual model is built, or the data is cleaned and prepared for exploration, or before data scientists start their work – this is where the data engineers come into action. A data-driven business must have a framework for the data science pipeline; otherwise, it’s a set up for failure.
A majority of the people who aspire to enter the data science world want to become data scientists, even without realizing a data engineer’s role. Data engineers are a crucial part of any data science project, and their demand is increasing exponentially across the world.
This guide will give you a detailed path to become a successful data engineer. So, without any further ado, let’s get into it.
What is Data Engineering, and Who is a Data Engineer?
Data engineering can be defined as a highly variable, big-tent domain having the main focus on building reliable mechanisms or infrastructure for the data collection.
A data engineer is someone who acts as a gatekeeper and facilitator for the seamless flow and storage of data. Data engineers are also responsible for transforming big data into a useful form for further analysis. For this transformation, they have to design, construct, install, test, and maintain scalable data management systems.
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Data Engineer vs. Data Scientist
At the core, a data engineer is responsible for developing and maintaining various architectures such as databases and large-scale processing systems. We can say that a data engineer deals with the raw data filled with human or instrumental errors. This data is often non-validated and unformatted.
On the other hand, a data scientist has to clean and organize the data for analysis and prediction. The data received by the data scientists have passed the first round of cleaning and manipulation. They have to process this data to be fed into machine learning algorithms for predictive and perspective modeling.
Data Engineer – Job Description
A data engineer is mainly responsible for handling the following tasks:
- Implementing, verifying, and designing software systems.
- Extracting data from one source and loading it into another with minimal errors.
- Working on multiple scripting languages and understanding the nuances to combine the systems efficiently.
- Finding out new ways to extract data and utilizing the existing data.
- Collaborating with other team members, like data architects, data analysts, and data scientists, for building robust data pipelines and systems.
With that said, let us find out how you can become a successful data engineer and grab your dream job.
Steps to Become a Data Engineer
1. Become proficient at programming
Before you start working on data engineering tools, you have to acquire the required skill set. To become a successful data engineer, you need to brush up on foundational programming skills.
The data science world primarily revolves around two technologies – Python and Scala. Therefore, you must know how to write scripts as well as create software in Python.
Scala, on the other hand, is built on strong functional programming foundations. It runs on the JVM and therefore is compatible with other Java libraries.
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2. Get in-depth knowledge of the database.
To be a data engineer, you have to have a very firm grip on the database languages and tools. This is amongst the very basic requirements if you are looking for a job for a data engineer. You should know how to collect, store, and query the information from the databases in real-time.
Some of the commonly used databases are:
– SQL Databases
– NoSQL Databases
You must gain in-depth knowledge of at least one of the databases mentioned above.
3. Data warehouse architecture
Almost every organization asks for data warehousing and ETL experience for the role of a data engineer. For data warehousing, we have tools such as Amazon Redshift, Microsoft Azure, Google BigQuery, Snowflake, etc. Some of the commonly used ETL tools are Xplenty, AWS Glue, Alooma, Oracle Data Integrator, etc.
4. Hadoop based Analytics
Companies demand a strong understanding of apache Hadoop-based analytics when you apply for a data engineer position. Therefore, you must know how to work on Hbase, Hive, or Mapreduce to land into your data engineer’s dream job efficiently.
5. Basic understanding of Machine Learning
Machine Learning is the branch of Artificial Intelligence that gives the machines the power to learn without being explicitly programmed. Python language is often used to design Machine Learning algorithms.
To get a competitive edge, you must have a basic knowledge of various machine learning algorithms. It will help you in creating effective pipelines for data collection and generation.
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6. Familiarity with using different operating systems
Each industry uses a different operating system based on their needs and preferences. Some like to work on Windows, while others prefer Unix and Linux. However, as far as data engineering is concerned, Unix and Linux are amongst the widely used operating systems.
Therefore, an aspiring data engineer must know how to use at least one of these operating systems.
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Becoming a data engineer is not a cakewalk, especially when you are new to the tech world. It demands a strong and in-depth knowledge of tools, tech, and work ethic to get a high paying data engineering job.
Having a professional certificate in your portfolio will give you a competitive edge in the market while increasing your chances of getting your dream job at your dream company.
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